The present disclosure generally relates to optical computing, and, more specifically, to optical computing devices and methods utilizing multiple integrated computational elements with increased detection sensitivity.
When making analytical measurements, sensitivity of the chosen analytical technique can often be problematic. As used in the analytical sciences and herein, the terms “sensitivity” or “detection sensitivity” can refer to two related factors: 1) the degree of change in analytical response that occurs per change in quantity of analyte, or 2) the minimum quantity of analyte that can be detected with a sufficient degree of confidence. The latter interpretation of “sensitivity” is also often referred to as the “detection limit.” Low sensitivity analyses can sometimes fail to detect low abundance analytes, to determine if an analyte quantity is increasing or decreasing (e.g., due to a treatment, chemical reaction, or the like), or to differentiate between two or more samples having similar quantities of analyte. Analyses of samples containing complex mixtures of substances can be particularly problematic when the chosen analytical technique has a low sensitivity.
Although analytical sensitivity can be a concern in a number of fields, oilfield operations, such as subterranean treatment operations, represent one instance where sensitive analyses can often be desirable. Subterranean treatment operations can include, without limitation, drilling operations, stimulation operations, production operations, remediation operations, and the like. Such subterranean treatment operations are generally conducted with a treatment fluid, which can contain a variety of components that may directly or indirectly affect the desired purpose of the treatment operation. As used herein, the terms “treat,” “treatment,” “treating,” and grammatical equivalents thereof refer to any subterranean operation that uses a fluid in conjunction with achieving a desired function and/or for a desired purpose. Use of these terms does not imply any particular action by the treatment fluid or a component thereof, unless otherwise specified herein.
Subterranean treatment operations are often quite susceptible to small changes in the quantity of one or more components present in a treatment fluid used to carry out the treatment operation. For example, a treatment fluid component that is present in an out-of-range amount may result in failure of a treatment operation and/or damage to a subterranean formation. Either of these outcomes are undesirable and can lead to increased costs and delayed production. Low-sensitivity analyses can exacerbate these issues, either by failing to identify out-of-range components before issues occur and/or barring proactive control of a treatment operation from taking place. Similar issues can be encountered in other process settings.
Spectroscopic analyses are well known for their sensitivity and versatility for detecting a wide variety of substances. Most spectroscopic instruments are general purpose and are not configured to detect any one particular substance or class of substance. That is, post-acquisition analysis of a sample's spectrum is usually conducted to determine the quantity of a substance of interest that is present. In addition, involved and time-consuming sample processing may also be needed to analyze for a particular substance with a given degree of accuracy and sensitivity, and the sample processing procedures may often vary considerably depending upon the nature of the sample undergoing analysis. Although spectroscopic analyses can be routinely carried out under laboratory conditions, they are considerably more difficult to transition into less controlled environments, such as the oilfield and other process settings, particularly when no or limited sample processing can be performed.
Optical computing devices represent an alternative to conventional spectroscopic equipment and analyses. Optical computing devices utilize an integrated computational element (ICE), also referred to as an “ICE core,” which is a processing element that is specifically designed to analyze for a given component or characteristic of interest in a sample upon optical interaction of electromagnetic radiation therewith. As used herein, the term “integrated computational element” will refer to an optical processing element containing a plurality of optical thin film layers formed from various materials whose indices of refraction and thicknesses may vary between each layer. The layer compositions, thicknesses, and ordering may be chosen, based upon calculations, to selectively transmit or reflect predetermined fractions of electromagnetic radiation at different wavelengths such that the integrated computational element is configured to substantially mimic a regression vector corresponding to a particular component or characteristic of interest in a sample. As used herein, the term “characteristic” will refer to a substance's concentration in a sample or a derived physical property for the sample. The transmission or reflection function of the integrated computational element may represent the regression vector for a characteristic of interest, and the transmission function may be weighted with respect to wavelength. Accordingly, upon optically interacting electromagnetic radiation with a sample and with an integrated computational element, the electromagnetic radiation changes in a known and specific way that may be representative of the characteristic's magnitude in the sample. Following receipt of the electromagnetic radiation by a detector, an output from the detector can be correlated to the characteristic of interest. Even though a complex mixture of substances may be present in a given sample, the integrated computational element may be able to distinguish and analyze for this substance based on its unique regression vector.
Optical computing devices may be advantageous compared to conventional spectroscopic techniques, since optical computing analyses may be conducted rapidly, often in real-time, with limited to no sample preparation involved. Rather than obtaining an optical spectrum, which may require further interpretation and deconvolution, the output of optical computing devices is a real number that is correlatable to a characteristic of interest. Optical computing devices are also much more rugged than conventional spectroscopic equipment and can be deployed in locales where spectroscopic analyses may otherwise be problematic. Accordingly, optical computing devices may often be desirable for analyzing complex mixtures in various process environments, such as those encountered in the oilfield industry. As a further advantage, optical computing devices can often provide high-sensitivity analyses for a variety of substances, although there remain instances where their analytical sensitivity may still be a limiting factor.